Overview

Dataset statistics

Number of variables22
Number of observations491
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.5 KiB
Average record size in memory176.3 B

Variable types

Categorical13
Numeric9

Alerts

AgeBaseline is highly overall correlated with eGFRBaselineHigh correlation
CreatinineBaseline is highly overall correlated with eGFRBaseline and 1 other fieldsHigh correlation
eGFRBaseline is highly overall correlated with AgeBaseline and 1 other fieldsHigh correlation
sBPBaseline is highly overall correlated with dBPBaselineHigh correlation
dBPBaseline is highly overall correlated with sBPBaselineHigh correlation
BMIBaseline is highly overall correlated with HistoryObesityHigh correlation
TimeToEventMonths is highly overall correlated with TIME_YEARHigh correlation
TIME_YEAR is highly overall correlated with TimeToEventMonthsHigh correlation
Sex is highly overall correlated with CreatinineBaselineHigh correlation
HistoryDiabetes is highly overall correlated with DMmedsHigh correlation
HistoryHTN is highly overall correlated with HTNmeds and 1 other fieldsHigh correlation
HistoryDLD is highly overall correlated with DLDmedsHigh correlation
HistoryObesity is highly overall correlated with BMIBaselineHigh correlation
DLDmeds is highly overall correlated with HistoryDLDHigh correlation
DMmeds is highly overall correlated with HistoryDiabetesHigh correlation
HTNmeds is highly overall correlated with HistoryHTN and 1 other fieldsHigh correlation
ACEIARB is highly overall correlated with HistoryHTN and 1 other fieldsHigh correlation
HistoryCHD is highly imbalanced (55.8%)Imbalance
HistoryVascular is highly imbalanced (67.6%)Imbalance
TimeToEventMonths has 6 (1.2%) zerosZeros
TIME_YEAR has 10 (2.0%) zerosZeros

Reproduction

Analysis started2023-11-20 15:09:14.462050
Analysis finished2023-11-20 15:09:31.705568
Duration17.24 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
250 
0
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

Length

2023-11-20T16:09:31.876113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:32.021724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

Most occurring characters

ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 250
50.9%
0 241
49.1%

AgeBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.203666
Minimum23
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:32.215206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile28
Q144
median54
Q364
95-th percentile75
Maximum89
Range66
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.821282
Coefficient of variation (CV)0.25978062
Kurtosis-0.64254322
Mean53.203666
Median Absolute Deviation (MAD)10
Skewness-0.23450907
Sum26123
Variance191.02782
MonotonicityNot monotonic
2023-11-20T16:09:32.428388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 17
 
3.5%
56 17
 
3.5%
54 16
 
3.3%
50 15
 
3.1%
44 14
 
2.9%
61 14
 
2.9%
64 14
 
2.9%
69 14
 
2.9%
51 13
 
2.6%
53 13
 
2.6%
Other values (49) 344
70.1%
ValueCountFrequency (%)
23 1
 
0.2%
24 3
 
0.6%
25 3
 
0.6%
26 8
1.6%
27 8
1.6%
28 3
 
0.6%
29 7
1.4%
30 6
1.2%
31 9
1.8%
32 5
1.0%
ValueCountFrequency (%)
89 1
 
0.2%
80 1
 
0.2%
79 6
1.2%
78 2
 
0.4%
77 4
0.8%
76 7
1.4%
75 5
1.0%
74 7
1.4%
73 4
0.8%
72 5
1.0%

HistoryDiabetes
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
276 
1
215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

Length

2023-11-20T16:09:32.635370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:32.798933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

Most occurring characters

ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 276
56.2%
1 215
43.8%

HistoryCHD
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
446 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

Length

2023-11-20T16:09:33.045277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:33.262696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 446
90.8%
1 45
 
9.2%

HistoryVascular
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
462 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

Length

2023-11-20T16:09:33.445209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:33.592812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 462
94.1%
1 29
 
5.9%

HistorySmoking
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
416 
1
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

Length

2023-11-20T16:09:33.747398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:33.887058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 416
84.7%
1 75
 
15.3%

HistoryHTN
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
335 
0
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

Length

2023-11-20T16:09:34.068691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:34.212095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

Most occurring characters

ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 335
68.2%
0 156
31.8%

HistoryDLD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
317 
0
174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

Length

2023-11-20T16:09:34.356417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:34.488252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

Most occurring characters

ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 317
64.6%
0 174
35.4%

HistoryObesity
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
248 
0
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

Length

2023-11-20T16:09:34.639848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:34.793438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

Most occurring characters

ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 248
50.5%
0 243
49.5%

DLDmeds
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
271 
0
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

Length

2023-11-20T16:09:34.957942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:35.103414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

Most occurring characters

ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 271
55.2%
0 220
44.8%

DMmeds
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
330 
1
161 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

Length

2023-11-20T16:09:35.284893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:35.447198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

Most occurring characters

ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 330
67.2%
1 161
32.8%

HTNmeds
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
303 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

Length

2023-11-20T16:09:35.614753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:35.768121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

Most occurring characters

ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 303
61.7%
0 188
38.3%

ACEIARB
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
272 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

Length

2023-11-20T16:09:35.941165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:36.095581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

Most occurring characters

ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272
55.4%
1 219
44.6%

CholesterolBaseline
Real number (ℝ)

Distinct70
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9785743
Minimum2.23
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:36.262884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.23
5-th percentile3.2
Q14.2
median5
Q35.77
95-th percentile6.7
Maximum9.3
Range7.07
Interquartile range (IQR)1.57

Descriptive statistics

Standard deviation1.0967017
Coefficient of variation (CV)0.22028429
Kurtosis0.051925448
Mean4.9785743
Median Absolute Deviation (MAD)0.8
Skewness0.19348603
Sum2444.48
Variance1.2027547
MonotonicityNot monotonic
2023-11-20T16:09:36.498287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 26
 
5.3%
5.8 20
 
4.1%
4.5 19
 
3.9%
4.3 18
 
3.7%
5.1 17
 
3.5%
5.3 17
 
3.5%
5.7 17
 
3.5%
5.2 16
 
3.3%
4.8 15
 
3.1%
5.9 15
 
3.1%
Other values (60) 311
63.3%
ValueCountFrequency (%)
2.23 1
 
0.2%
2.4 2
 
0.4%
2.56 1
 
0.2%
2.6 1
 
0.2%
2.8 2
 
0.4%
2.9 7
1.4%
2.96 1
 
0.2%
3 5
1.0%
3.1 4
0.8%
3.2 3
0.6%
ValueCountFrequency (%)
9.3 1
 
0.2%
8.2 1
 
0.2%
8 1
 
0.2%
7.8 1
 
0.2%
7.7 1
 
0.2%
7.5 3
0.6%
7.4 1
 
0.2%
7.3 3
0.6%
7.2 1
 
0.2%
7.1 2
0.4%

CreatinineBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.856823
Minimum6
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:36.715464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile41.5
Q155
median66
Q378.5
95-th percentile101
Maximum123
Range117
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation17.918627
Coefficient of variation (CV)0.26406522
Kurtosis-0.11656027
Mean67.856823
Median Absolute Deviation (MAD)12
Skewness0.27020997
Sum33317.7
Variance321.07719
MonotonicityNot monotonic
2023-11-20T16:09:36.931853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 21
 
4.3%
63 15
 
3.1%
69 14
 
2.9%
64 14
 
2.9%
71 13
 
2.6%
66 13
 
2.6%
57 11
 
2.2%
49 11
 
2.2%
73 10
 
2.0%
76 10
 
2.0%
Other values (79) 359
73.1%
ValueCountFrequency (%)
6 1
 
0.2%
27 1
 
0.2%
29 2
0.4%
30 1
 
0.2%
31 2
0.4%
33 1
 
0.2%
35 3
0.6%
37 2
0.4%
38 2
0.4%
39 1
 
0.2%
ValueCountFrequency (%)
123 1
0.2%
113 2
0.4%
111 1
0.2%
110 1
0.2%
109 1
0.2%
108 1
0.2%
107 2
0.4%
106 2
0.4%
105 1
0.2%
104 2
0.4%

eGFRBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct324
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.11609
Minimum60
Maximum242.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:37.149916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile67.75
Q186.4
median98.1
Q3109.5
95-th percentile126.7
Maximum242.6
Range182.6
Interquartile range (IQR)23.1

Descriptive statistics

Standard deviation18.503267
Coefficient of variation (CV)0.18858545
Kurtosis6.7231652
Mean98.11609
Median Absolute Deviation (MAD)11.4
Skewness0.97699064
Sum48175
Variance342.3709
MonotonicityNot monotonic
2023-11-20T16:09:37.373549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.1 5
 
1.0%
105.5 5
 
1.0%
90.1 5
 
1.0%
93.3 4
 
0.8%
109.5 4
 
0.8%
90.4 4
 
0.8%
87.7 4
 
0.8%
100.6 4
 
0.8%
104.3 4
 
0.8%
87.3 4
 
0.8%
Other values (314) 448
91.2%
ValueCountFrequency (%)
60 1
0.2%
60.4 1
0.2%
60.7 1
0.2%
62.2 1
0.2%
62.4 1
0.2%
63.2 1
0.2%
63.3 1
0.2%
63.5 2
0.4%
63.6 2
0.4%
64.2 1
0.2%
ValueCountFrequency (%)
242.6 1
0.2%
148.6 1
0.2%
144.1 1
0.2%
139.7 1
0.2%
139 1
0.2%
138.7 1
0.2%
137.2 1
0.2%
136.3 1
0.2%
135.8 1
0.2%
134.8 1
0.2%

sBPBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.37475
Minimum92
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:37.636424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile107
Q1121
median131
Q3141
95-th percentile160.5
Maximum180
Range88
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.69265
Coefficient of variation (CV)0.11944952
Kurtosis0.056712757
Mean131.37475
Median Absolute Deviation (MAD)10
Skewness0.38188046
Sum64505
Variance246.25928
MonotonicityNot monotonic
2023-11-20T16:09:37.905741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 21
 
4.3%
132 17
 
3.5%
121 16
 
3.3%
133 15
 
3.1%
120 15
 
3.1%
123 15
 
3.1%
140 15
 
3.1%
118 14
 
2.9%
126 13
 
2.6%
124 13
 
2.6%
Other values (67) 337
68.6%
ValueCountFrequency (%)
92 1
 
0.2%
95 1
 
0.2%
99 1
 
0.2%
100 6
1.2%
101 1
 
0.2%
103 3
0.6%
104 2
 
0.4%
105 3
0.6%
106 4
0.8%
107 5
1.0%
ValueCountFrequency (%)
180 1
 
0.2%
177 1
 
0.2%
176 2
0.4%
174 1
 
0.2%
173 1
 
0.2%
171 1
 
0.2%
168 1
 
0.2%
167 2
0.4%
166 2
0.4%
165 3
0.6%

dBPBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.87169
Minimum41
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:38.185674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile60
Q169
median77
Q383
95-th percentile95
Maximum112
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.711134
Coefficient of variation (CV)0.13933783
Kurtosis0.38394467
Mean76.87169
Median Absolute Deviation (MAD)7
Skewness0.13844313
Sum37744
Variance114.7284
MonotonicityNot monotonic
2023-11-20T16:09:38.482913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 24
 
4.9%
74 21
 
4.3%
82 20
 
4.1%
75 20
 
4.1%
77 19
 
3.9%
78 19
 
3.9%
68 18
 
3.7%
76 18
 
3.7%
79 18
 
3.7%
69 17
 
3.5%
Other values (47) 297
60.5%
ValueCountFrequency (%)
41 1
 
0.2%
45 1
 
0.2%
46 1
 
0.2%
53 2
 
0.4%
54 2
 
0.4%
55 2
 
0.4%
57 5
1.0%
58 3
 
0.6%
59 1
 
0.2%
60 9
1.8%
ValueCountFrequency (%)
112 2
0.4%
110 1
0.2%
108 1
0.2%
107 1
0.2%
103 1
0.2%
102 1
0.2%
101 1
0.2%
100 2
0.4%
99 2
0.4%
98 2
0.4%

BMIBaseline
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.183299
Minimum13
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:38.730774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q126
median30
Q333
95-th percentile42
Maximum57
Range44
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.2367415
Coefficient of variation (CV)0.20662889
Kurtosis1.6974345
Mean30.183299
Median Absolute Deviation (MAD)4
Skewness0.96041043
Sum14820
Variance38.896945
MonotonicityNot monotonic
2023-11-20T16:09:39.325818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
30 43
 
8.8%
27 42
 
8.6%
28 38
 
7.7%
32 34
 
6.9%
31 34
 
6.9%
24 33
 
6.7%
25 31
 
6.3%
29 27
 
5.5%
33 27
 
5.5%
26 22
 
4.5%
Other values (27) 160
32.6%
ValueCountFrequency (%)
13 1
 
0.2%
16 1
 
0.2%
17 2
 
0.4%
18 1
 
0.2%
19 3
 
0.6%
21 13
 
2.6%
22 7
 
1.4%
23 22
4.5%
24 33
6.7%
25 31
6.3%
ValueCountFrequency (%)
57 1
 
0.2%
53 1
 
0.2%
52 3
0.6%
51 1
 
0.2%
49 2
0.4%
47 2
0.4%
46 2
0.4%
45 3
0.6%
44 3
0.6%
43 3
0.6%

TimeToEventMonths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.065173
Minimum0
Maximum111
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:39.536588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q177
median93
Q3100
95-th percentile106
Maximum111
Range111
Interquartile range (IQR)23

Descriptive statistics

Standard deviation26.01114
Coefficient of variation (CV)0.31314135
Kurtosis1.8987856
Mean83.065173
Median Absolute Deviation (MAD)9
Skewness-1.6421144
Sum40785
Variance676.57942
MonotonicityNot monotonic
2023-11-20T16:09:39.765442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 24
 
4.9%
99 20
 
4.1%
105 19
 
3.9%
93 19
 
3.9%
94 19
 
3.9%
96 18
 
3.7%
98 17
 
3.5%
88 17
 
3.5%
101 17
 
3.5%
104 16
 
3.3%
Other values (83) 305
62.1%
ValueCountFrequency (%)
0 6
1.2%
3 2
 
0.4%
4 1
 
0.2%
5 1
 
0.2%
7 1
 
0.2%
8 3
0.6%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
111 1
 
0.2%
109 2
 
0.4%
108 9
1.8%
107 12
2.4%
106 10
2.0%
105 19
3.9%
104 16
3.3%
103 9
1.8%
102 13
2.6%
101 17
3.5%

EventCKD35
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
435 
1
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

Length

2023-11-20T16:09:40.058625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:40.232956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 435
88.6%
1 56
 
11.4%

TIME_YEAR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9409369
Minimum0
Maximum9
Zeros10
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-20T16:09:40.391571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median8
Q38
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2017095
Coefficient of variation (CV)0.31720639
Kurtosis1.793421
Mean6.9409369
Median Absolute Deviation (MAD)1
Skewness-1.5801472
Sum3408
Variance4.8475248
MonotonicityNot monotonic
2023-11-20T16:09:40.563668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 196
39.9%
9 91
18.5%
7 79
16.1%
6 34
 
6.9%
4 25
 
5.1%
5 18
 
3.7%
1 14
 
2.9%
3 12
 
2.4%
2 12
 
2.4%
0 10
 
2.0%
ValueCountFrequency (%)
0 10
 
2.0%
1 14
 
2.9%
2 12
 
2.4%
3 12
 
2.4%
4 25
 
5.1%
5 18
 
3.7%
6 34
 
6.9%
7 79
16.1%
8 196
39.9%
9 91
18.5%
ValueCountFrequency (%)
9 91
18.5%
8 196
39.9%
7 79
16.1%
6 34
 
6.9%
5 18
 
3.7%
4 25
 
5.1%
3 12
 
2.4%
2 12
 
2.4%
1 14
 
2.9%
0 10
 
2.0%

Interactions

2023-11-20T16:09:29.162047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.122468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.494753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.794006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.060208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:21.733865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.366838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:25.392596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:27.772979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.311447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.277283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.648140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.935350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.213582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:21.911981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.534390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:25.636700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:27.985412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.463044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.428293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.786699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.074595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.368102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:22.080166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.707708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:25.917419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.169915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.599677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.571028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.926667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.196672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.505245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:22.229793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.879251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:26.066021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.293132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.747840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.747416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.085219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.339842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.646695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:22.478627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:24.100539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:26.233750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.433914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.929354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:16.912703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.250724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.459514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.780993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:22.726183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:24.478579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:26.468125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.573191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:30.136798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.081681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.388200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.603966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:20.944399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:22.909863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:24.719566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:26.958327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.776258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:30.434570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.208402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.523611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.764301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:21.066842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.058465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:24.944962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:27.311576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:28.911782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:30.704421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:17.353962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:18.668447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:19.926902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:21.198640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:23.199677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:25.186148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:27.523582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:29.041407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-20T16:09:40.732038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AgeBaselineCholesterolBaselineCreatinineBaselineeGFRBaselinesBPBaselinedBPBaselineBMIBaselineTimeToEventMonthsTIME_YEARSexHistoryDiabetesHistoryCHDHistoryVascularHistorySmokingHistoryHTNHistoryDLDHistoryObesityDLDmedsDMmedsHTNmedsACEIARBEventCKD35
AgeBaseline1.000-0.0670.102-0.6540.189-0.105-0.1170.0230.0180.1990.3520.2460.1910.0000.3020.3760.2200.3890.2740.3490.2710.250
CholesterolBaseline-0.0671.000-0.0990.0580.0210.0600.1340.0170.0130.0890.1320.2000.1870.0390.0000.2430.1000.1870.2030.0970.0770.149
CreatinineBaseline0.102-0.0991.000-0.6890.0570.034-0.143-0.185-0.1850.6560.0940.1620.2720.2280.1070.0870.0000.1050.1130.1210.1690.267
eGFRBaseline-0.6540.058-0.6891.000-0.1600.0570.0610.1280.1280.1800.2940.2550.3080.0500.3030.3180.0980.3370.2370.3320.2940.387
sBPBaseline0.1890.0210.057-0.1601.0000.6000.136-0.041-0.0570.0000.1930.1750.0000.1990.4870.0000.1660.1050.1400.3350.2580.093
dBPBaseline-0.1050.0600.0340.0570.6001.0000.1890.0100.0160.0920.0000.0920.0000.0790.2690.0000.1880.0000.0480.1450.1210.141
BMIBaseline-0.1170.134-0.1430.0610.1360.1891.0000.0260.0220.2550.0000.0000.2070.1740.0920.1080.8560.1010.0000.0000.0000.000
TimeToEventMonths0.0230.017-0.1850.128-0.0410.0100.0261.0000.9620.1660.1590.1890.0820.1240.1800.1120.0000.1650.2200.1460.1240.473
TIME_YEAR0.0180.013-0.1850.128-0.0570.0160.0220.9621.0000.1660.1510.2020.0140.1500.1640.0000.0230.0760.2280.1230.1320.486
Sex0.1990.0890.6560.1800.0000.0920.2550.1660.1661.0000.0200.1280.1070.3860.0000.0000.1970.0000.0690.0000.0870.077
HistoryDiabetes0.3520.1320.0940.2940.1930.0000.0000.1590.1510.0201.0000.2200.0480.0000.3040.3290.0340.3930.7870.3420.3240.281
HistoryCHD0.2460.2000.1620.2550.1750.0920.0000.1890.2020.1280.2201.0000.0720.0250.1420.1930.0000.2180.2480.1790.1710.249
HistoryVascular0.1910.1870.2720.3080.0000.0000.2070.0820.0140.1070.0480.0721.0000.0870.0750.0000.1520.0000.0580.0890.0650.074
HistorySmoking0.0000.0390.2280.0500.1990.0790.1740.1240.1500.3860.0000.0250.0871.0000.0000.0000.0960.0000.0000.0000.0000.076
HistoryHTN0.3020.0000.1070.3030.4870.2690.0920.1800.1640.0000.3040.1420.0750.0001.0000.2260.1350.3280.2630.8620.6070.163
HistoryDLD0.3760.2430.0870.3180.0000.0000.1080.1120.0000.0000.3290.1930.0000.0000.2261.0000.0760.8180.2550.2850.2720.131
HistoryObesity0.2200.1000.0000.0980.1660.1880.8560.0000.0230.1970.0340.0000.1520.0960.1350.0761.0000.0430.0000.0940.0460.000
DLDmeds0.3890.1870.1050.3370.1050.0000.1010.1650.0760.0000.3930.2180.0000.0000.3280.8180.0431.0000.3170.3880.3400.156
DMmeds0.2740.2030.1130.2370.1400.0480.0000.2200.2280.0690.7870.2480.0580.0000.2630.2550.0000.3171.0000.3200.3440.299
HTNmeds0.3490.0970.1210.3320.3350.1450.0000.1460.1230.0000.3420.1790.0890.0000.8620.2850.0940.3880.3201.0000.7020.151
ACEIARB0.2710.0770.1690.2940.2580.1210.0000.1240.1320.0870.3240.1710.0650.0000.6070.2720.0460.3400.3440.7021.0000.222
EventCKD350.2500.1490.2670.3870.0930.1410.0000.4730.4860.0770.2810.2490.0740.0760.1630.1310.0000.1560.2990.1510.2221.000

Missing values

2023-11-20T16:09:30.954750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T16:09:31.492139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SexAgeBaselineHistoryDiabetesHistoryCHDHistoryVascularHistorySmokingHistoryHTNHistoryDLDHistoryObesityDLDmedsDMmedsHTNmedsACEIARBCholesterolBaselineCreatinineBaselineeGFRBaselinesBPBaselinedBPBaselineBMIBaselineTimeToEventMonthsEventCKD35TIME_YEAR
0064000011110104.859.093.314487409808
1052000011100106.452.0105.8148914510609
2056000011110106.457.099.814986418807
3058000001110005.165.090.3116683210309
4063100011111115.070.079.7132633110509
5051000011110114.957.0103.4124843710509
6071100011100115.659.088.812572369808
7044000001100006.669.092.712077449308
8054000001110005.063.096.512379408807
9044100011110115.754.0110.6157873610309
SexAgeBaselineHistoryDiabetesHistoryCHDHistoryVascularHistorySmokingHistoryHTNHistoryDLDHistoryObesityDLDmedsDMmedsHTNmedsACEIARBCholesterolBaselineCreatinineBaselineeGFRBaselinesBPBaselinedBPBaselineBMIBaselineTimeToEventMonthsEventCKD35TIME_YEAR
481125100001011005.8080.0117.613688286405
482176111011011104.4096.065.91536123410
483170110111010104.7064.094.3145702110509
484127000000000004.4097.091.813679215204
485161000011110114.20104.066.512682329308
486125000000000005.3073.0122.6127742410209
487145000010100005.9067.0110.3150112339408
488144100001111006.4166.3111.61268335000
489141001111010114.0093.087.611966223513
490124000000000002.2365.0129.5121552110609